ChaosSearch’s Post

Mosaic AI is a suite of tools that allows Databricks users to build, manage, and deploy software solutions that incorporate AI, ML, and large language model (LLM) technologies. Mosaic AI is fully integrated within the Databricks Data Intelligence Platform, which provides a single solution for storing data in a unified data lakehouse, training AI and machine learning models, and deploying those AI/ML solutions in production. Databricks Mosaic AI encompasses the following products: 💠 Mosaic AI Vector Search - A queryable vector database integrated with the Databricks Platform, Mosaic AI Vector Search is used in LLM solutions to store and retrieve mathematical representations of the semantic contents of text or image data. 💠 Mosaic AI Agent Framework - A set of Databricks tools that allow developers to build, deploy, and evaluate AI agents using Retrieval Augmented Generation (RAG), an AI design technique that augments an existing LLM with an external knowledge base. 💠Mosaic AI Model Serving - A solution for deploying LLMs and accessing Gen-AI models, including open LLMs (via Foundation Model APIs) and external LLMs hosted outside Databricks. 💠Mosaic AI Gateway - A tool for managing the usage of Gen-AI models, Mosaic AI Gateway delivers monitoring, governance, and production readiness features like usage tracking, access permissions, and traffic routing. 💠Mosaic AI Model Training - An AI model training solution that allows users to customize open-source LLMs or cost-effectively train new ones using enterprise data. 💠 Feature Store - A solution for creating, publishing, and re-using features used to train ML models or feed batch inference pipelines. 💠 Databricks AutoML - Databricks AutoML is a solution that provides a low-code approach to building, training, and deploying ML models. 💠 MLflow - MLflow is an open-source platform used to manage artifacts and workflows throughout the MLOps pipeline - from initial model development and training, through to deployment and operation. 💠Lakehouse Monitoring - A tool for monitoring data quality in the data lakehouse, Lakehouse Monitoring can also be used to track the performance of ML models and model-serving endpoints. Though not technically a Mosaic AI product, Databricks Unity Catalog is another important service that provides centralized discovery, management, and governance of models and data stored in the Databricks lakehouse. Learn more about Databricks Mosaic AI use cases: https://lnkd.in/dy4-aXCV

Enterprise Generative AI applications are fundamentally different from running a model in a Google notebook. As I delve deeper into developing such applications, I increasingly recognize the critical importance of optimized data engineering within a company. In a few months, LLMs will become commoditized. The companies that will thrive without their own foundational models are those that know how to structure and store their data effectively. Data is the key, and efficient data storage has never been more important now. Having data catalogs in place is essential, especially when managing multiple data systems. What is data catalog? A data catalog is a detailed inventory of all data assets in an organization, designed to help data professionals quickly find the most appropriate data for any analytical or business purpose. Enterprises often have different departments with varied data storage solutions. Some may use S3 buckets, while others rely solely on Postgres. To navigate this complexity, you should: 1. Create Data Catalogs: Develop a data catalog for each of your data sources. 2. Implement Adaptive Agents: Create an adaptive agent that regularly updates your data catalog schema. 3. Adopt a Sample Data Strategy: Use a sample data strategy to test your models. Ensure your sample data is probabilistically similar to your real-time data by employing an agent that updates your sample data. 4. Use Efficient Data Formats: Store data in efficient formats like Parquet and follow best practices. 5. Avoid Data Lakes as Dumping Grounds: The way you store data significantly impacts how you can use it to build your AI systems. 6. Embrace Apache Iceberg: Adopt Apache Iceberg as soon as possible to enhance data management and performance. Optimizing your data engineering processes is not just a necessity; it’s an advantage in the competitive landscape of AI development. P.S. I have been building working on developing enterprise grade generative AI systems. At HTCD following these principles we have build a generative AI cloud observability platform that will cover your enterprise observability without spending thousands of dollars. We are also looking for talented full stack engineers who want to work with us in this domain. [Apply directly from our website]

Corralling Data for Generative AI: From Chaos to Creativity Generative AI thrives on well-organized, high-quality data. To unlock its potential, organizations must master the art of data corralling — the process of gathering, cleaning, and preparing data for AI applications using advanced technologies and tools. Why Data Corralling Matters for Generative AI Model Performance: Clean, diverse data ensures generative AI models like GPT or DALL-E produce accurate and creative outputs. Efficiency: Streamlined data pipelines accelerate model training and deployment cycles. Ethical AI: Well-prepared data mitigates biases and ensures compliance with frameworks like GDPR and CCPA. HITL tools and AI Fairness 360. Key Steps to Effective Data Corralling Source Smartly: Use web scraping tools like BeautifulSoup or APIs like Twitter API to collect structured and unstructured data, including text, images, and audio. look into Hugging face transformers ,ResNet,SIFT , ORB etc . Automate Cleaning: Leverage tools such as OpenRefine or Pandas for data cleaning, deduplication, and format standardization. Centralize Access: Implement data lakes or warehouses using platforms like Databricks lakehouse,Snowflake or AWS S3 for efficient storage and retrieval. Enforce Governance: Use data governance tools like Collibra ,Allation or Talend to define policies for data usage, access control, and retention. Challenges and Solutions Data Silos: Integrate disparate systems using ETL tools like Apache NiFi or Informatica. Bias Risks: Employ fairness-enhancing technologies such as IBM’s AI Fairness 360 to audit and balance datasets. Resource Gaps: Utilize scalable cloud services like Google Cloud AI or Azure ML for cost-effective infrastructure and processing power. Conclusion Corralling data effectively is essential for harnessing generative AI’s capabilities. By utilizing cutting-edge tools, ensuring robust data governance, and prioritizing ethical considerations, organizations can drive innovation and achieve transformative AI outcomes while maintaining trust and compliance. #fintech #GENAI #AI #data #Apache #Azure #googlecloud #cloud #AWS #womenintechnology

Yes, both a #datalake and a #datawarehouse offer flexible #data storage options – but they’re used for different purposes. 🤔Which would you choose for complex analytics and reporting? 🤖Which would you choose to support #AI, #ML, and #datascience? Find out all the essential details, plus how to #integrate and #automate data changes for the most efficient, trustworthy data pipelines. Learn more: https://hubs.li/Q02ygZ7T0 #Liquibase #DatabaseDevOps #DatabaseCICD #DBA #Developer #DevOps #CICD

Transforming unstructured text into structured formats like JSON has become a critical capability for businesses across industries. From financial services (extracting key details from financial documents) to healthcare (extracting patient information from medical records) and e-commerce (extracting sentiment from customer comments), structured extraction unlocks valuable insights from massive volumes of unstructured data. In my latest technical blog post, I demonstrate how to perform structured extraction at scale: > Leverage the Databricks Foundational Model API with Llama 3.1 70B for structured output. > Use AI_QUERY for high-performance batch inference. > Complete the process end-to-end on the Databricks Mosaic AI platform. Read the full tutorial here: https://lnkd.in/gHAdzzuH Let me know how you're approaching structured extraction in your projects!

💡 Clean, curated data in an enterprise data warehouse is essential for successful GenAI projects. Stay ahead by investing in advanced ETL / data pipeline tools. MIT Technology Review findings: 🔍 82% of execs prioritize scaling AI/GenAI. 🔍 83% have identified data sources but struggle with integration and security. 🔍 82% prioritize data integration and data movement solutions that will continue to work in the future, regardless of changes to their data strategy and partners. #BigData #GenerativeAI #AI #DataIntegration More from BigDATAwire: https://lnkd.in/g6aaxFcW

The Benefits of Using Structured Output from OpenAI over JSON In today’s data-driven world, structured data is crucial for efficient data handling, especially when integrating AI into workflows and applications. While JSON has become a de facto standard for representing structured data, OpenAI’s new structured output feature offers distinct advantages for those working with complex AI outputs. 1. Enhanced Clarity and Precision JSON is known for its flexibility but often leaves room for ambiguity. OpenAI's structured output leverages specific schemas that dictate exactly how the data should be structured and labeled. This built-in validation ensures that the response strictly follows the specified structure, reducing the likelihood of errors or misinterpretations. 2. Minimized Post-Processing With JSON outputs, developers frequently need to parse, validate, and sometimes reformat the data before it’s usable in the application. Structured outputs eliminate this step by providing results in a format that aligns directly with the defined schema. 3. Reduced Errors in Complex Scenarios JSON’s flexibility can sometimes backfire when handling complex data that needs strict structuring, such as nested data or multi-step responses. The structured output feature enables developers to outline complex, hierarchical schemas that JSON alone can’t easily enforce. 4. Improved Readability for Developers OpenAI’s structured output provides a predefined and organized format that’s easier for developers to read and understand, as it removes unnecessary clutter and adheres strictly to the schema. JSON, while straightforward, can become difficult to parse in complex datasets, leading to errors in data handling. Structured output makes it clear exactly what data can be expected, enhancing readability and helping developers quickly understand how to integrate responses into their applications. 5. Streamlined Error Handling and Debugging Error handling in JSON responses often requires additional validation to catch null values, mismatched types, or unexpected structures. OpenAI’s structured output minimizes these issues by providing a controlled environment where outputs are consistently formatted. 6. Scalability for Larger Applications For organizations that use AI across multiple departments or large-scale applications, consistency is key. Structured output provides a reliable format that different teams and services can depend on, making it easier to scale applications and maintain interoperability between systems. Conclusion While JSON will always have its place, Open AI’s structured output represents a significant improvement for handling complex data in AI-driven applications. With its emphasis on clarity, precision, and minimized need for post-processing, structured output is a valuable tool for developers aiming to integrate AI more seamlessly into their workflows.

Here's your LinkedIn post draft on "AI Tools That Can Be Useful in the Field of Data Analysis" along with the image prompt: AI Tools That Can Be Useful in the Field of Data Analysis Data analysis is transforming at a rapid pace, thanks to the rise of Artificial Intelligence. The good news? AI tools are making it easier and more efficient to uncover insights, patterns, and predictions in complex datasets. Here's a look at some must-have AI tools for data analysts: 🔹 Google Cloud AutoML For those looking to leverage machine learning but without deep coding expertise, Google Cloud AutoML is a game-changer. It allows you to build custom machine learning models with ease—ideal for handling large datasets and generating insights. 🔹 IBM Watson Analytics IBM’s Watson offers advanced analytics, automated data visualization, and predictive analysis. It’s designed to allow data scientists to focus on high-level tasks while Watson handles data cleaning, model building, and result interpretation. 🔹 Tableau with AI Integration Tableau’s AI integration allows data analysts to dive deeper into datasets with Predictive Analysis and Natural Language Processing. It’s a perfect blend of interactive data visualization and AI-powered insights. 🔹 RapidMiner RapidMiner is an open-source platform that simplifies data science and machine learning. It’s especially great for automating repetitive tasks, like data cleaning and modeling, allowing you to focus on creating actionable insights from your data. 🔹 DataRobot For more advanced AI-driven automation, DataRobot automates the building, testing, and deployment of machine learning models. It’s built for data scientists who want to save time and focus on extracting business value from predictive models. 🔹 H2O.ai An open-source AI platform that helps with building scalable machine learning models. Whether you’re a beginner or an expert, H2O.ai enables you to use algorithms, without coding, to unlock the value hidden in your data. 🔹 Qlik Sense with AI With AI and machine learning, Qlik Sense provides self-service analytics with a focus on uncovering hidden insights from complex data, helping analysts ask the right questions and make smarter decisions. These AI tools not only speed up the analysis process but also bring higher accuracy, insights, and actionable outcomes to the table. Which of these AI tools are you most excited to explore? Let’s talk about how AI is shaping the future of data analysis in the comments!

Low-Code/No-Code in ML, Data Science, and Gen AI 🌟 Absolutely! Low-Code/No-Code (LCNC) tools are making a big impact in Machine Learning (ML), Data Science, and Generative AI. These platforms are democratizing access to AI, enabling users without deep coding skills to build, deploy, and use AI-powered solutions. How LCNC is Transforming AI and Data Science --- 1️⃣ Simplifying ML Model Building 🧠 Platforms like DataRobot, H2O.ai, and Google AutoML allow users to build and train models with minimal code. Drag-and-drop interfaces help automate tasks like data preprocessing, feature selection, and model deployment. 2️⃣ Automated Data Science Pipelines 📊 Tools like Alteryx, KNIME, and RapidMiner enable users to create end-to-end data workflows visually. This makes tasks like data wrangling, visualization, and analysis quicker and more accessible to non-technical users. 3️⃣ Generative AI Integration 🤖 LCNC platforms are integrating Gen AI models (like GPT, Stable Diffusion) to simplify tasks such as text generation, image creation, or summarization. Examples: Microsoft Power Platform integrates GPT models for text-based automation, while Akkio enables AI-powered predictions with zero coding. 4️⃣ Citizen Data Scientists 👩💻 With tools like Microsoft Azure ML, Amazon SageMaker Autopilot, and IBM Watson, non-coders can leverage AI to make data-driven decisions. This bridges the gap between domain experts and data science, enabling teams to solve problems faster. 5️⃣ Speeding up Prototyping and Deployment ⚡ Low-code MLOps tools are emerging, allowing quick deployment and monitoring of machine learning models. Tools like MLFlow, Streamlit, or Databricks simplify versioning and sharing of ML projects. Examples of Tools Revolutionizing AI with LCNC Google AutoML: Train custom machine learning models with minimal effort. Azure ML Designer: A drag-and-drop platform for creating and deploying ML models. DataRobot: Automates ML processes, from data prep to model deployment. Alteryx: Enables data blending, analytics, and machine learning workflows visually. Streamlit: Build interactive ML-powered applications using minimal Python code. Power BI with AI: Generate predictive insights using built-in AI models. LCNC + Generative AI = The Future 🚀 LCNC platforms are integrating Gen AI models like GPT-4 to automate tasks like content generation, chatbot development, and data summarization. Example: Bubble and Zapier enable building AI-powered workflows without any coding. But Will LCNC Replace AI/ML Engineers? 🤔 Not really! LCNC tools automate simpler or repetitive tasks, but for complex problems, custom models, and scalability, coding and expertise are irreplaceable. Engineers will focus on innovative, customized solutions while LCNC tools handle routine workflows. What are your thoughts? Have you explored any Low-Code/No-Code tools for ML or AI yet? Let’s discuss below! 👇

Databricks leverages GenAI in both internal and external support teams. Use cases include: - Provide support teams better documentation and knowledge - Infuse #GenAI into existing technologies to help IT support - Leverage copilots to build tools, dashboards, and ML models We are extensive users of DatabricksIQ and assistant copilots to speed up data engineering, data ingestion, reporting, and other data tasks. Additional uses of copilots extend to language migration, test case development, and code explanation. The productivity gains make a noticeable difference to our business, with increases of up to 30% in some cases. Explore the experimental approaches we’re taking and some of the biggest results.